Agile machine learning: From theory to production
With artificial intelligence and machine learning becoming increasingly relevant for modern enterprises, many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve. In this session, Sumanas Sarma and Rob Hinds explain how you can go from theory to production in adopting machine learning solutions.
Artificial intelligence (AI) and machine learning (ML) are all the rage right now. In this session, we took a look at engineering best practices that can be applied to ML, how ML research can be integrated with an agile development cycle, and how open-ended research can be managed within project planning.
Many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve, let alone how it might fit into a traditional engineering team or how they might get it to a production setting.
The speakers explain that at Basement Crowd how they took a new product to market and went from a simple idea to a production ML system. Along the way, we have had to integrate open-ended academic research tasks with our existing agile development process and project planning, as well as working out how to deliver the ML system to a production setting in a repeatable, robust way, with all the considerations expected from a normal software project.
Rob Hinds is currently an Engineering Team Lead at legal-tech startup BasementCrowd. Having previously spent 4 years working at an investment management start-up in London, building a cutting-edge trading platform and marketplace for money managers and prior to that, 6+ years working as a technical consultant at Accenture, working on a range of technology-driven, client-facing projects based across Europe.
Sumanas Sarma is currently on a work placement at Basement Crowd whilst completing a Masters in Software Engineering from Queen Mary University of London. His focus in the past year has been on Machine Learning applications and before that, he spent 4 years at a start-up in the finance industry using Groovy.